This article describes challenges and benefits of modeling learner variability in Computer-Assisted Language Learning. We discuss the learner model of E-Tutor, a learner model that addresses learner variability by focusing on certain aspects and/or features of the learner's interlanguage. Moreover, we introduce the concept of phrase descriptors, the means by which the student model of E-Tutor captures very detailed linguistic information on the learner's performance and progress. Finally, we provide longitudinal data that emphasize the importance of monitoring fine-grained information and underline the dynamism and non-linearity of the SLA process, as also described by Dynamic Systems Theory (DST).
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Trude Heift (2008) studied this question.
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